Write a Blog >>
EASE 2021
Mon 21 - Thu 24 June 2021
Mon 21 Jun 2021 15:15 - 15:35 at Zoom - Software Quality II Chair(s): Paolo Arcaini

Due to its enormous benefits, the research and industry communities have shown an increasing interest in the Microservices Architecture (MSA) style over the last few years. Despite this, there is a limited evidence-based and thorough understanding of the types of issues (e.g., faults, errors, failures, mistakes) faced by microservices system developers and causes that trigger the issues. Such evidence-based understanding of issues and causes is vital for longterm, impactful, and quality research and practice in the MSA style. To that end, we conducted an empirical study on 1,345 issue discussions extracted from five open source microservices systems hosted on GitHub. Our analysis led to the first of its kind taxonomy of the types of issues in open source microservices systems, informing that the problems originating from Technical debt (321, 23.86%), Build (145, 10.78%), Security (137, 10.18%), and Service execution and communication (119, 8.84%) are prominent. We identified that “General programming errors”, “Poor security management”, “Invalid configuration and communication”, and “Legacy versions, compatibility and dependency” are the predominant causes for the leading four issue categories. Study results streamline a taxonomy of issues, their mapping with underlying causes, and present empirical findings that could facilitate research and development on emerging and next-generation microservices systems.

Mon 21 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:15 - 16:15
Software Quality IIEASE 2021 / Vision and Emerging Results Track at Zoom
Chair(s): Paolo Arcaini National Institute of Informatics
On the Nature of Issues in Five Open Source Microservices Systems: An Empirical Study
EASE 2021
Muhammad Waseem Wuhan University, China, Peng Liang Wuhan University, Mojtaba Shahin Monash University, Aakash Ahmad , Ali Rezaei Nasab
Pre-print Media Attached
DABT: A Dependency-aware Bug Triaging Method
EASE 2021
Hadi Jahanshahi Ryerson University, Kritika Chhabra Ryerson University, Mucahit Cevik Ryerson University, Ayşe Başar Ryerson University
Vision and Emerging Results
SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain
Vision and Emerging Results Track
Sohil Lal Shrestha The University of Texas at Arlington, Christoph Csallner University of Texas at Arlington
DOI Pre-print Media Attached